Electric utility companies regularly collect usage data from meters. Over the last few decades, many public utilities have modernized their data collection using automated meter reading to reduce the time and expense of collecting this data. More recently, utilities have responded to the dynamic pricing of electricity by shifting from once-a-month meter readings to daily, hourly or even sub-hourly meter readings. The daily readings are called register, anchor, or check reads, and the hourly and sub-hourly readings, which frequently report cumulative usage rather than readings on the meter, are called interval reads.
This shift in meter reading frequency is conceptually simple. However, for larger utilities with millions of customers, implementing the shift has been and continues to be an intensely complex undertaking as they transition their business processes and supporting computer infrastructure from handling millions of monthly reads per month to potentially handling billions per month. The amount of data is staggering and the opportunities for making mistakes in gathering it accurately and reliably, even with the most advanced wired and wireless automated meter reading technologies, is equally staggering. One need only consider the problems of signal quality and dropped calls in using mobile telephones, for example, to understand that even the best and most reliable meter data collection systems inevitably suffer from missing or inaccurate reads.
To facilitate the management of this fantastic amount of data, many utilities are now using meter data management systems (MDMSs). One of the leading providers of such systems is Ecologic Analytics, LLC of Bloomington, Minn., the employer of the present inventors.
One problem recognized by the present inventors is that some MDMSs estimate missing register reads using past average daily usage (ADU) values that are derived from past daily (register) reads. Although this estimation procedure is often effective in allowing estimation of the daily register read and daily usage value, it also frequently results in failures of a test known as a sum check because the daily usage estimate based on past ADU does not always match the actual usage that can be determined from good interval reads for the same day. When the difference between these two usage values for the same period of time differs by more than a certain allowable amount, this condition is deemed a sum check exception.
Sum checks are frequently required by public utility commissions to ensure that bills based on automated reads, that is, reads collected without on-site human readings, are accurate. The commissions may monitor check sum exceptions and/or require utility companies to investigate and resolve each of them. Thus, utilities incur expense for every sum check exception and these expenses ultimately diminish the savings from using automated meter readings and may even diminish utility satisfaction and confidence in the MDMS.
Accordingly, the present inventors have recognized a need for new ways of improving the reliability and accuracy of estimated usage data and reducing sum check exceptions.